We first test the English (\(\mathbf {en}\) ) Google Speech Command [1]} corpus, containing a total of up to \(11,165\)  training and \(6,500\)  testing utterances in 1-second length. Next, for the low-resource spoken command recognition task we have four languages: Georgian (\(\mathbf {ga}\) ) with 1458 utterances and Chuvash (\(\mathbf {cv}\) ) with 706 utterances are collected from the Mozilla Common Voice data set [2]} with the same 10 frequent commands translating from the above set of English words. Meanwhile, Arabic (\(\mathbf {ar}\) ) [3]} and Lithuanian [4]} (\(\mathbf {lt}\) ) Speech Commands are referred to the setup in the existing spoken command classification studies. Arabic includes 1600 utterances with 6 smart home control words and 10 spoken digits (0 through 9). For Georgian, Chuvash, and Arabic data, we use the same setup as in [3]} to split these sets into 80% for training and \(20\%\)  for testing with 10-fold cross-validation. Lithuanian duses 326 , 75 and 88 utterances for training, validation and testing, respectively. In Figure REF  we display the maximum training set sizes for each language used. Clearly there is a noticeable difference between English and the other four low-resource languages.
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